It may be a concern but little noise can't affect the final results if the algorithm is stable in numerical. The MKLDNN backend with mxnet-mkl has been used for 2 years and we didn't see the coverage issue caused by multiple threading. In other words, GPU programming mode works well on training where the non-deterministic also exists from multiple threads.
Parts of training accuracy was pasted in the first PR when MKLDNN is integrated. https://github.com/apache/incubator-mxnet/pull/8302#issuecomment-359674818 In conclusion, it may happen with very little probability. I believe we can get a solution in case it happens someday. Thanks, --Patric > -----Original Message----- > From: Chris Olivier <cjolivie...@gmail.com> > Sent: Tuesday, November 19, 2019 11:51 AM > To: dev@mxnet.incubator.apache.org > Cc: Tao Lv <mutou...@gmail.com> > Subject: Re: Proposal to make MKLDNN as default CPU backend > > (for non mkl dropout, for instance) > > On Mon, Nov 18, 2019 at 7:50 PM Chris Olivier <cjolivie...@gmail.com> > wrote: > > > To address the deterministic item, I know for a fact that training > > will not be deterministic in some cases where the “parallel random” > > class is utilized in parallel threads, such as OMP, if the number of > > cores is different, even with the same seed, because threads are > > seeded independently and different number of threads will end up > > generating different random number sequences. Dropout operator being > an example. > > > > On Mon, Nov 18, 2019 at 6:39 PM Alfredo Luque > > <alfredo.lu...@airbnb.com.invalid> wrote: > > > >> For AMD CPUs, you’d want to perform validation because now MKL-DNN > >> would be enabled by default. Historically, other intel libraries > >> (along with the ICC > >> compiler) have had performance issues on AMD CPUs. It’s just worth > >> double checking to make sure that’s not the case here. Perhaps some > >> MKL-DNN authors can chime in though. It’s not sufficient to double > >> check that an > >> AVX2 package passes tests. > >> > >> Agreed in the case we’re not releasing ARM binaries. > >> > >> The reproducibility argument is around the results being numerically > >> reproducible. That is, eg; if I train a model with some fixed set of > >> data, some random seed, etc. and then run inference on it do I get > >> the exact same floating point values for the weights and results? > >> Does MxNet already offer this without MKL-DNN? > >> > >> On November 18, 2019 at 6:32:07 PM, Tao Lv (mutou...@gmail.com) > wrote: > >> > >> Regarding the cases listed by Marco: > >> - AMD CPU > >> From my architecture knowledge, what works on C4 instances (with AVX2 > >> support) should also work well on m5a, right? I think mxnet-mkl and > >> mxnet-cuxxmkl packages have been fully validated on AVX2 machines. > >> Also, we didn't perform any validation on AMD CPU before, why we need > >> do that for this time? > >> > >> - ARM CPU > >> I don't know we're releasing any convenience binaries for ARM CPU. > >> This proposal mainly targets those pypi packages. > >> > >> - Windows > >> Already validated by CI. We're also releasing mxnet-mkl packages for Win. > >> > >> - GPU and MKLDNN enabled > >> Already validated by CI and mxnet-cuxxmkl packages have been released > >> for several versions. > >> > >> - Fully reproducible results (medical and financial sector requested > >> that and we have some flags for cuda) Not sure I understand this > >> case. We already have MKL-DNN backend for a while. Functionality and > >> correctness of it have been verified by MXNet users. > >> > >> -tao > >> > >> On Tue, Nov 19, 2019 at 4:41 AM Marco de Abreu > >> <marco.g.ab...@gmail.com> > >> wrote: > >> > >> > Sorry, my intent with the "non-standard" phrase was not about > >> > general > >> MXNet > >> > but rather from MKLDNNs point of view, considering that it's being > >> > developed by Intel, I assumed that MKLDNN might consider non-intel > >> > use-cases non standard. > >> > > >> > -Marco > >> > > >> > Skalicky, Sam <sska...@amazon.com.invalid> schrieb am Mo., 18. Nov. > >> 2019, > >> > 21:34: > >> > > >> > > Thanks Alfredo, if you can create a GitHub issue with notes/steps > >> > > we > >> can > >> > > add this to the todo list for integrating with the MXNet CI to > >> > > test on > >> > m5a > >> > > instances too. Then we can start tracking this on a regular > >> > > basis. It > >> > would > >> > > be great to actually test on ARM instances now that AWS has A1 > >> instances > >> > > too…..ill add it to the wish list ;-D > >> > > > >> > > Sam > >> > > > >> > > > On Nov 18, 2019, at 12:32 PM, Alfredo Luque < > >> alfredo.lu...@airbnb.com > >> > .INVALID> > >> > > wrote: > >> > > > > >> > > > Happy to run some benchmarks on an AWS m5a instance (Epyc) and > >> > > > first generation AMD Threadripper Gen 1 if someone has > >> > > > something easy to > >> run > >> > > and > >> > > > representative. > >> > > > > >> > > > On November 18, 2019 at 12:29:31 PM, Skalicky, Sam ( > >> > > > sska...@amazon.com.invalid) wrote: > >> > > > > >> > > > Thanks a good idea Alfredo, are you able to help test on AMD CPUs? > >> Or > >> > is > >> > > > there someone else in the mxnet dev@ community who can help? > >> > > > > >> > > > Sam > >> > > > > >> > > >> On Nov 18, 2019, at 12:27 PM, Alfredo Luque > >> > > > <alfredo.lu...@airbnb.com.INVALID> wrote: > >> > > >> > >> > > >> Verifying that there isn’t a slowdown on AMD CPUs (eg; Ryzen / > >> Epyc) > >> > > > would > >> > > >> definitely make sense as a requirement. It seems odd to > >> > > >> classify > >> that > >> > as > >> > > > a > >> > > >> “nonstandard” use case. > >> > > >> > >> > > >> On November 18, 2019 at 12:20:33 PM, Skalicky, Sam ( > >> > > >> sska...@amazon.com.invalid) wrote: > >> > > >> > >> > > >> Thanks Patric & team for your work over the years to make > >> > > >> MXNet > >> fast > >> > > with > >> > > >> MKLDNN! > >> > > >> > >> > > >> I think it would be great to make MKLDNN enabled by default. > >> > > >> We > >> will > >> > > need > >> > > >> to continue producing variants without MKLDNN for those who > >> > > >> don’t > >> want > >> > > it > >> > > >> (Marco enumerated some use cases). How do you propose to > >> > > >> identify > >> the > >> > > pip > >> > > >> wheels with/without MKLDNN? Previously we had: mxnet-mkl and > >> > > > mxnet-cu101mkl > >> > > >> with MKLDNN. If the plain “mxnet” pip wheel now contains > >> > > >> MKLDNN > >> what > >> > do > >> > > > you > >> > > >> propose we call the build without MKLDNN? mxnet-nomkl? > >> > > >> > >> > > >> Thanks! > >> > > >> Sam > >> > > >> > >> > > >>> On Nov 18, 2019, at 11:08 AM, Marco de Abreu < > >> > marco.g.ab...@gmail.com> > >> > > >> wrote: > >> > > >>> > >> > > >>> Hi Patric, > >> > > >>> > >> > > >>> First of all, thanks a lot to you and your team for all the > >> > > >>> effort > >> on > >> > > >> MXNet > >> > > >>> and mkldnn! > >> > > >>> > >> > > >>> Generally I'm inclined towards your proposal, but I'm > >> > > >>> thinking > >> about > >> > > the > >> > > >>> non-standard use cases: > >> > > >>> - AMD CPU > >> > > >>> - ARM CPU > >> > > >>> - Windows > >> > > >>> - GPU and MKLDNN enabled > >> > > >>> - Fully reproducible results (medical and financial sector > >> requested > >> > > > that > >> > > >>> and we have some flags for cuda) > >> > > >>> > >> > > >>> Is mkldnn fully compatible with these use cases? If not, what > >> would > >> > > >> happen? > >> > > >>> If yes, do we have performance numbers? > >> > > >>> > >> > > >>> Best regards, > >> > > >>> Marco > >> > > >>> > >> > > >>> Zhao, Patric <patric.z...@intel.com> schrieb am Mo., 18. Nov. > >> 2019, > >> > > >> 14:00: > >> > > >>> > >> > > >>>> Hi MXNet community, > >> > > >>>> > >> > > >>>> From the first MKLDNN backend integrated in release 1.2, the > >> > community > >> > > >> is > >> > > >>>> continuously improving the quality and performance of MKLDNN > >> > > >>>> CPU > >> > > >> backend. > >> > > >>>> Nowadays, the MKLDNN backend is widely used for the > >> > > >>>> inference, > >> > > >> especially > >> > > >>>> for INT8 inference, and we got lots of very positive > >> > > >>>> feedbacks > >> from > >> > > >> MXNet > >> > > >>>> users. > >> > > >>>> > >> > > >>>> Achieved milestones as below: > >> > > >>>> > >> > > >>>> - MKLDNN integrated into Apache MXNet from release 1.2, Feb, > >> > > >>>> 2018 > >> > [1] > >> > > >>>> - MKLDNN backend as default CPU backend from source > >> > > >>>> building, > >> Jan, > >> > > 2019 > >> > > >> [2] > >> > > >>>> - MKLDNN subgraph optimization as default for the inference, > >> > > >>>> Jul, > >> > 2019 > >> > > >> [3] > >> > > >>>> - MKLDNN major version upgrade in release 1.6, Oct, 2019 [4] > >> > > >>>> > >> > > >>>> To make more successful and technical leadership for Apache > >> > > >>>> MXNet > >> in > >> > > > the > >> > > >>>> industry, I propose to make MKLDNN as default CPU backend in > >> > > >>>> all > >> > > binary > >> > > >>>> distribution from the next release. > >> > > >>>> The new milestone includes: > >> > > >>>> > >> > > >>>> - Static link MKLDNN library in the binary avoiding the > >> > > >>>> mismatch > >> > > > version > >> > > >>>> in the runtime [5] > >> > > >>>> - Make nightly build with MKLDNN default from master pre 1.7 > >> release > >> > > >>>> - Binary distribution with MKLDNN default from 1.7 release. > >> > > >>>> > >> > > >>>> What will be changed: > >> > > >>>> > >> > > >>>> - mxnet and mxnet-cuXX binary will be built with MKLDNN=1 > >> > > >>>> - mxnet-mkl and mxnet-cuXXmkl will be not changed in the > >> > > >>>> minor > >> > release > >> > > >>>> (1.x) and plan to remove in next major release (2.0) > >> > > >>>> > >> > > >>>> Suggestions and comments are highly appreciated. > >> > > >>>> > >> > > >>>> Thanks, > >> > > >>>> > >> > > >>>> --Patric > >> > > >>>> > >> > > >>>> > >> > > >>>> [1] https://github.com/apache/incubator-mxnet/pull/9677 > >> > > >>>> [2] > >> > > >>>> > >> > > >> > >> > > > > >> > > > >> > > >> > >> > https://lists.apache.org/thread.html/bfeae6ee46374112eb4dff1470c26295 > >> 9101e4bffb19930926963535@%3Cdev.mxnet.apache.org%3E > >> > > >>>> [3] https://github.com/apache/incubator-mxnet/pull/15518 > >> > > >>>> [4] > >> > > >>>> > >> > > >> > >> > > > > >> > > > >> > > >> > >> > https://lists.apache.org/thread.html/f46ab920f18795496eafe713e6e9e561 > >> c684e06189085cec17b401dc@%3Cdev.mxnet.apache.org%3E > >> > > >>>> [5] https://github.com/apache/incubator-mxnet/pull/16731 > >> > > >>>> > >> > > >> > >> > > >> — > >> > > >> Alfredo Luque > >> > > >> Software Engineer > >> > > >> Machine Learning Infrastructure Airbnb San Francisco, CA > >> > > > > >> > > > — > >> > > > Alfredo Luque > >> > > > Software Engineer > >> > > > Machine Learning Infrastructure Airbnb San Francisco, CA > >> > > > >> > > > >> > > >> > >> — > >> Alfredo Luque > >> Software Engineer > >> Machine Learning Infrastructure > >> Airbnb > >> San Francisco, CA > >> > >
